UAV Depth Data Artifact Correction
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in accurately interpreting their surroundings due to artifacts in depth data, which can lead to incorrect navigation and safety risks, especially in urban environments.
Innovation Solution
A method and system for identifying and deleting or adjusting incorrect depth data by determining the boundary between image portions with significant depth data differences and undefined data, using a sensor unit to capture images with depth data, and processing these images to mitigate risks associated with artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors capture depth data of surrounding environment, then navigation capability is improved, but artifacts cause incorrect depth data leading to navigation errors
Solution Approach 1:
The patent applies preliminary action by detecting and marking artifact regions in depth data before navigation decisions are made. The control unit identifies areas with undefined depth data or abnormal depth values and marks them as artifacts, preventing these incorrect measurements from affecting subsequent navigation operations. This pre-processing step ensures that navigation relies only on valid depth data.
Solution Approach 2:
The patent introduces an intermediary processing layer between the sensor and navigation system. The control unit acts as a mediator that receives raw depth data, processes it through artifact detection algorithms, and outputs corrected depth data to the navigation system. This intermediary step filters out incorrect measurements while preserving valid information, resolving the contradiction between maintaining measurement coverage and ensuring accuracy.
2Measurement precision
If artifact detection and correction is implemented, then depth data accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the depth data processing into distinct stages: initial artifact detection based on undefined depth data, secondary artifact detection based on abnormal depth values, and final correction/interpolation. This segmented approach allows each processing stage to focus on specific types of artifacts using tailored algorithms, improving accuracy while keeping individual processing steps relatively simple and manageable.
Data Source
AI summary
A method for improving the interpretation of the surroundings of a UAV, and to a UAV system is presented herein. The method comprises the steps of acquiring an image comprising depth data, and determining a boundary between a first image portion and a second image portion. The second image portion surrounds the first image portion, and the boundary is defined by an interface between first periphery sub-portions and second periphery sub-portions. A difference in depth data between adjacent first periphery sub-portions and second periphery sub-portions is above a first predetermined threshold and/or second periphery sub-portions comprises undefined depth data. The method further comprises determining the area of the first image portion, and if the area of the first image portion is below a second predetermined threshold, determining that the first image portion contains incorrect depth data and deleting and/or adjusting the incorrect depth data contained by the first image portion.